Multipoint and significance methods for genome-wide association studies

全基因组关联研究的多点和显着性方法

• 批准号: 7101305
• 负责人: MATTHEW STEPHENS
• 金额: $ 29.23万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-06-15 至 2007-05-24
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7101305
• 关键词: antihypercholesterolemic agent; atherosclerosis; atherosclerotic plaque; clinical research; computer data analysis; computer human interaction; computer program /software; computer system design /evaluation; cooperative study; gene expression; gene interaction; genetic screening; human data; method development; pharmacogenetics; single nucleotide polymorphism; statistics /biometry; thrombosis

DESCRIPTION: (provided by applicant) This project will develop statistical methods for analyzing both genome-wide association studies and studies on multiple candidate genes, where the phenotype of interest is quantitative. The methods will include novel multipoint methods designed to extract the maximum amount of information from the available data, and methods for assessing significance of the results that deal effectively with the large number of multiple comparisons being performed in these large-scale studies. The proposed multipoint approach to association mapping are motivated by the fact that, even with a genome-wide scan of 250,000 SNPs, many SNPs affecting phenotype will be untyped. The idea is to assess whether an untyped SNP affects phenotype by first using surrounding haplotypic variation to predict plausible genotypes at the untyped SNP, and then assessing association between the predicted genotypes and observed phenotypes. The methods for assessing significance will be based on controlling the "False Discovery Rate" (the proportion of positive findings that turn out to be incorrect). The methods will be applied to a genome-wide scan (250,000 SNPs in 1,000 individuals) and candidate gene studies aimed at identifying genetic variants and genes responsible for differential response to statin drugs, and to data from a candidate gene study aimed at identifying genetic variants affecting quantitative phenotypes associated with atherosclerosis, plaque inflammation, and thrombosis, all factors associated with cardio-vascular disease. Findings from these studies may aid understanding of the genetic factors affecting cardio-vascular disease, and its treatment. In addition, user friendly software implementing the statistical methods will be developed and distributed, allowing other researchers conducting similar studies world-wide to have access to these tools. These tools have the potential to improve the effectiveness and efficiency of studies aimed at determining the underlying genetic basis of common diseases, potentially leading to new treatment strategies for maintaining health and preventing disease. Public health relevance: This project will generate statistical tools for analyzing large-scale studies that aim to help understand the genetic basis of common diseases and drug response. These tools have the potential to improve the effectiveness and efficiency of such studies, potentially leading to new treatment strategies for maintaining health and preventing disease.

描述：（由申请人提供）该项目将开发统计方法，用于分析全基因组关联研究和多个候选基因的研究，其中感兴趣的表型是定量的。这些方法将包括旨在从可用数据中提取最大量信息的新型多点方法，以及评估结果显着性的方法，以有效处理这些大规模研究中进行的大量多重比较。所提出的多点关联作图方法的动机是，即使对 250,000 个 SNP 进行全基因组扫描，许多影响表型的 SNP 也将是非分型的。这个想法是通过首先使用周围的单倍型变异来预测未分型的SNP的合理基因型，然后评估预测的基因型和观察到的表型之间的关联来评估未分型的SNP是否影响表型。评估重要性的方法将基于控制“错误发现率”（被证明是不正确的阳性发现的比例）。这些方法将应用于全基因组扫描（1,000 个个体中的 250,000 个 SNP）和候选基因研究，旨在识别遗传变异和对他汀类药物差异反应负责的基因，以及旨在识别遗传变异的候选基因研究的数据影响与动脉粥样硬化、斑块炎症和血栓形成相关的定量表型，所有这些因素都与心血管疾病相关。这些研究的结果可能有助于了解影响心血管疾病的遗传因素及其治疗。此外，将开发和分发实施统计方法的用户友好软件，使世界各地进行类似研究的其他研究人员能够访问这些工具。这些工具有可能提高旨在确定常见疾病潜在遗传基础的研究的有效性和效率，从而有可能产生维持健康和预防疾病的新治疗策略。公共卫生相关性：该项目将生成用于分析大规模研究的统计工具，旨在帮助了解常见疾病和药物反应的遗传基础。这些工具有可能提高此类研究的有效性和效率，有可能带来维持健康和预防疾病的新治疗策略。